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Functional division of the dorsal striatum based on a graph neural network

Authors :
Qian Zheng
Xiaojuan Ba
Yiyang Xin
Jiaofen Nan
Xiao Cui
Lin Xu
Source :
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 2470-2487 (2024)
Publication Year :
2024
Publisher :
AIMS Press, 2024.

Abstract

The dorsal striatum, an essential nucleus in subcortical areas, has a crucial role in controlling a variety of complex cognitive behaviors; however, few studies have been conducted in recent years to explore the functional subregions of the dorsal striatum that are significantly activated when performing multiple tasks. To explore the differences and connections between the functional subregions of the dorsal striatum that are significantly activated when performing different tasks, we propose a framework for functional division of the dorsal striatum based on a graph neural network model. First, time series information for each voxel in the dorsal striatum is extracted from acquired functional magnetic resonance imaging data and used to calculate the connection strength between voxels. Then, a graph is constructed using the voxels as nodes and the connection strengths between voxels as edges. Finally, the graph data are analyzed using the graph neural network model to functionally divide the dorsal striatum. The framework was used to divide functional subregions related to the four tasks including olfactory reward, "0-back" working memory, emotional picture stimulation, and capital investment decision-making. The results were further subjected to conjunction analysis to obtain 15 functional subregions in the dorsal striatum. The 15 different functional subregions divided based on the graph neural network model indicate that there is functional differentiation in the dorsal striatum when the brain performs different cognitive tasks. The spatial localization of the functional subregions contributes to a clear understanding of the differences and connections between functional subregions.

Details

Language :
English
ISSN :
15510018 and 89219503
Volume :
21
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Mathematical Biosciences and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.0dbbfdd146de4cb8b139d5ed89219503
Document Type :
article
Full Text :
https://doi.org/10.3934/mbe.2024109?viewType=HTML